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What is business intelligence? Y
our guide to BI and why
it matters
tableau.com
/learn/articles/business-intelligence
Business intelligence (BI) combines business analytics, data mining,
data visualization
, data
tools and infrastructure, and best practices to help organizations to make more data-driven
decisions. In practice, you know you’ve got modern business intelligence when you have a
comprehensive view of your organization’s data and use that data to drive change, eliminate
inefficiencies, and quickly adapt to market or supply changes.
It’s important to note that this is a very modern definition of BI—and BI has had a strangled
history as a buzzword. Traditional Business Intelligence, capital letters and all, originally
emerged in the 1960s as a system of sharing information across organizations. It further
developed in the 1980s alongside computer models for decision-making and turning data
into insights before becoming specific offering from BI teams with IT-reliant service
solutions. Modern BI solutions prioritize flexible self-service analysis, governed data on
trusted platforms, empowered business users, and speed to insight.
This article will serve as an introduction to BI and is the tip of the iceberg.
Examples of business intelligence
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Tableau's
Explain Data feature
helps to quickly identify possible explanations of outliers
and trends in data.
Much more than a specific “thing,” business intelligence is rather an umbrella term that
covers the processes and methods of collecting, storing, and analyzing data from business
operations or activities to optimize performance. All of these things come together to create a
comprehensive view of a business to help people make better, actionable decisions.
Over the past few years, business intelligence has evolved to include more processes and
activities to help improve performance. These processes include:
Data mining:
Using databases, statistics and machine learning to uncover trends in
large datasets.
Reporting:
Sharing data analysis to stakeholders so they can draw conclusions and
make decisions.
Performance metrics and benchmarking:
Comparing current performance data
to historical data to track performance against goals, typically using customized
dashboards.
Descriptive analytics:
Using preliminary data analysis to find out what happened.
Querying:
Asking the data specific questions, BI pulling the answers from the
datasets.
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Statistical analysis:
Taking the results from descriptive analytics and further
exploring the data using statistics such as how this trend happened and why.
Data visualization:
Turning data analysis into visual representations such as charts,
graphs, and histograms to more easily consume data.
Visual analysis:
Exploring data through visual storytelling to communicate insights
on the fly and stay in the flow of analysis.
Data preparation:
Compiling multiple data sources, identifying the dimensions and
measurements, preparing it for data analysis.
Why is business intelligence important?
Great BI helps businesses and organizations ask and answer questions of their data.
Business intelligence can help companies make better decisions by showing present and
historical data within their business context. Analysts can leverage BI to provide performance
and competitor benchmarks to make the organization run smoother and more efficiently.
Analysts can also more easily spot market trends to increase sales or revenue. Used
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effectively, the right data can help with anything from compliance to hiring efforts.
A few ways that business intelligence can help companies make smarter, data-
driven decisions:
Identify ways to increase profit
Analyze customer behavior
Compare data with competitors
Track performance
Optimize operations
Predict success
Spot market trends
Discover issues or problems
How business intelligence works
Businesses and organizations have questions and goals. To answer these questions and track
performance against these goals, they gather the necessary data, analyze it, and determine
which actions to take to reach their goals.
On the technical side, raw data is collected from the business’s activity. Data is processed and
then stored in data warehouses. Once it’s stored, users can then access the data, starting the
analysis process to answer business questions.
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How BI, data analytics, and business analytics work together
Business intelligence includes data analytics and business analytics, but uses them only as
parts of the whole process. BI helps users draw conclusions from data analysis. Data
scientists dig into the specifics of data, using advanced statistics and predictive analytics to
discover patterns and forecast future patterns. Data analytics asks “Why did this happen and
what can happen next?” Business intelligence takes those models and algorithms and breaks
the results down into actionable language.
According to Gartner's IT glossary, “business analytics includes data mining, predictive
analytics, applied analytics, and statistics.” In short, organizations conduct business analytics
as part of their larger business intelligence strategy. BI is designed to answer specific queries
and provide at-a-glance analysis for decisions or planning. However, companies can use the
processes of analytics to continually improve follow-up questions and iteration.
Business analytics shouldn’t be a linear process because answering one question will likely
lead to follow-up questions and iteration. Rather, think of the process as a cycle of data
access, discovery, exploration, and information sharing. This is called the cycle of analytics, a
modern term explaining how businesses use analytics to react to changing questions and
expectations.
The difference between traditional BI and modern BI
Modern BI prioritizes self-service analytics and speed to insight.
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